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Record W1963925180 · doi:10.1159/000161058

Cortical Serotonin 1A Receptor Levels Are Associated with Depression in Patients with Dementia with Lewy Bodies and Parkinson’s Disease Dementia

2008· article· en· W1963925180 on OpenAlexaff
Sally I. Sharp, Clive Ballard, Iryna Ziabreva, Margaret A. Piggott, Robert H. Perry, Elaine K. Perry, Dag Aarsland, Uwe Ehrt, Jan Larsen, Paul T. Francis

Bibliographic record

VenueDementia and Geriatric Cognitive Disorders · 2008
Typearticle
Languageen
FieldNeuroscience
TopicNeurotransmitter Receptor Influence on Behavior
Canadian institutionsCentre for Movement Disorders
FundersMedical Research CouncilNorges ForskningsrådUniversitetet i Bergen
KeywordsDementia with Lewy bodiesDementiaInternal medicinePsychologyEndocrinologyParkinson's diseaseDepression (economics)Alzheimer's diseaseMedicineDisease

Abstract

fetched live from OpenAlex

BACKGROUND: Serotonin 1A receptors (5-HT(1A)) have not been studied in dementia with Lewy bodies (DLB) or Parkinson's disease dementia (PDD) patients with depression. AIM: To examine 5-HT(1A) in DLB and PDD postmortem in relation to depression. METHODS: [(3)H]8-hydroxy-2-dipropylaminotetralin binding to 5-HT(1A) was determined in temporal cortex (Brodmann areas, BA20 and BA36) from 10 DLB patients, 17 PDD patients and 9 controls. RESULTS: 5-HT(1A) density was significantly higher in BA36 in combined DLB/PDD patients with depression, but was unaltered in BA20. CONCLUSION: Higher BA36 5-HT(1A) density in PDD and DLB patients than in control is dependent on whether the patient had experienced depression during life, not DLB/PDD diagnosis. A 5-HT(1A) antagonist adjuvant may improve treatment of depression in dementia.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.015
GPT teacher head0.230
Teacher spread0.215 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations55
Published2008
Admission routes1
Has abstractyes

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